The reliability and safety of the train system is a critical issue, as it transports many passengers in its daily operation. Most studies focus on fault diagnosis methods to determine the cause of faults in the train system. Aside from fault diagnosis, it is also vital to perceive a fault even before it occurs. In this study, a fault occurrence prediction based on a machine learning model is developed. The fault occurrence prediction method aims to predict the remaining useful life (RUL) of a train subsystem. RUL refers to the remaining amount of time before a fault occurs on a train subsystem. The prediction method developed in this study can be used to clear a fault even before it occurs. In case of inevitable faults, the output from the prediction method can be used to alert the personnel in charge by imposing an alarm. Therefore, the fault occurrence prediction method is expected to increase the reliability of the train system. The deep neural-network-based model is tested on an actual device. Deep neural network is used because of its feature extraction capability, especially in handling big amount of data. The testing results in 90.08% accuracy. In addition, a graphical user interface is developed as an interface between a user and the actual device containing the fault occurrence prediction model.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep-Learning-Based Fault Occurrence Prediction of Public Trains in South Korea


    Additional title:

    Transportation Research Record


    Contributors:


    Publication date :

    2022-02-02




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    High speed trains for Korea

    British Library Online Contents | 1997


    South West Trains

    Balmforth, John | SLUB | 2011


    Diesel trains for South America

    Engineering Index Backfile | 1937


    Diesel trains for South America

    Engineering Index Backfile | 1937